# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask
# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
%matplotlib inline
warnings.filterwarnings("ignore")
nltk.download('all')
[nltk_data] Downloading collection 'all' [nltk_data] | [nltk_data] | Downloading package abc to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package abc is already up-to-date! [nltk_data] | Downloading package alpino to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package alpino is already up-to-date! [nltk_data] | Downloading package biocreative_ppi to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package biocreative_ppi is already up-to-date! [nltk_data] | Downloading package brown to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package brown is already up-to-date! [nltk_data] | Downloading package brown_tei to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package brown_tei is already up-to-date! [nltk_data] | Downloading package cess_cat to [nltk_data] | C:\Users\pawan\AppData\Roaming\nltk_data... [nltk_data] | Package cess_cat is already up-to-date! 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[nltk_data] | [nltk_data] Done downloading collection all
True
# path = '/content/drive/MyDrive/Files/'
path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
df_tvshows = pd.read_csv(path + 'otttvshows.csv')
df_tvshows.head()
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | Language | Plotline | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Snowpiercer | 2013 | 18+ | 6.9 | 94% | NaN | Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso... | Action,Drama,Sci-Fi,Thriller | United States | English | Set seven years after the world has become a f... | 60.0 | tv series | 3.0 | 1 | 0 | 0 | 0 | 1 |
| 1 | 2 | Philadelphia | 1993 | 13+ | 8.8 | 80% | NaN | Charlie Day,Glenn Howerton,Rob McElhenney,Kait... | Comedy | United States | English | The gang, 5 raging alcoholic, narcissists run ... | 22.0 | tv series | 18.0 | 1 | 0 | 0 | 0 | 1 |
| 2 | 3 | Roma | 2018 | 18+ | 8.7 | 93% | NaN | Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ... | Action,Drama,History,Romance,War | United Kingdom,United States | English | In this British historical drama, the turbulen... | 52.0 | tv series | 2.0 | 1 | 0 | 0 | 0 | 1 |
| 3 | 4 | Amy | 2015 | 18+ | 7.0 | 87% | NaN | Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma... | Drama | United States | English | A family drama focused on three generations of... | 60.0 | tv series | 6.0 | 1 | 0 | 1 | 1 | 1 |
| 4 | 5 | The Young Offenders | 2016 | NaN | 8.0 | 100% | NaN | Alex Murphy,Chris Walley,Hilary Rose,Dominic M... | Comedy | United Kingdom,Ireland | English | NaN | 30.0 | tv series | 3.0 | 1 | 0 | 0 | 0 | 1 |
# profile = ProfileReport(df_tvshows)
# profile
def data_investigate(df):
print('No of Rows : ', df.shape[0])
print('No of Coloums : ', df.shape[1])
print('**'*25)
print('Colums Names : \n', df.columns)
print('**'*25)
print('Datatype of Columns : \n', df.dtypes)
print('**'*25)
print('Missing Values : ')
c = df.isnull().sum()
c = c[c > 0]
print(c)
print('**'*25)
print('Missing vaules %age wise :\n')
print((100*(df.isnull().sum()/len(df.index))))
print('**'*25)
print('Pictorial Representation : ')
plt.figure(figsize = (10, 10))
sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
plt.show()
data_investigate(df_tvshows)
No of Rows : 5432
No of Coloums : 20
**************************************************
Colums Names :
Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
dtype='object')
**************************************************
Datatype of Columns :
ID int64
Title object
Year int64
Age object
IMDb float64
Rotten Tomatoes object
Directors object
Cast object
Genres object
Country object
Language object
Plotline object
Runtime float64
Kind object
Seasons float64
Netflix int64
Hulu int64
Prime Video int64
Disney+ int64
Type int64
dtype: object
**************************************************
Missing Values :
Age 1954
IMDb 556
Rotten Tomatoes 4194
Directors 5158
Cast 486
Genres 323
Country 549
Language 638
Plotline 2493
Runtime 1410
Seasons 679
dtype: int64
**************************************************
Missing vaules %age wise :
ID 0.000000
Title 0.000000
Year 0.000000
Age 35.972018
IMDb 10.235641
Rotten Tomatoes 77.209131
Directors 94.955817
Cast 8.946981
Genres 5.946244
Country 10.106775
Language 11.745214
Plotline 45.894698
Runtime 25.957290
Kind 0.000000
Seasons 12.500000
Netflix 0.000000
Hulu 0.000000
Prime Video 0.000000
Disney+ 0.000000
Type 0.000000
dtype: float64
**************************************************
Pictorial Representation :
# ID
# df_tvshows = df_tvshows.drop(['ID'], axis = 1)
# Age
df_tvshows.loc[df_tvshows['Age'].isnull() & df_tvshows['Disney+'] == 1, "Age"] = '13'
# df_tvshows.fillna({'Age' : 18}, inplace = True)
df_tvshows.fillna({'Age' : 'NR'}, inplace = True)
df_tvshows['Age'].replace({'all': '0'}, inplace = True)
df_tvshows['Age'].replace({'7+': '7'}, inplace = True)
df_tvshows['Age'].replace({'13+': '13'}, inplace = True)
df_tvshows['Age'].replace({'16+': '16'}, inplace = True)
df_tvshows['Age'].replace({'18+': '18'}, inplace = True)
# df_tvshows['Age'] = df_tvshows['Age'].astype(int)
# IMDb
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].mean()}, inplace = True)
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].median()}, inplace = True)
df_tvshows.fillna({'IMDb' : "NA"}, inplace = True)
# Rotten Tomatoes
df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].astype(int)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].mean()}, inplace = True)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].median()}, inplace = True)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'].astype(int)
df_tvshows.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
# Directors
# df_tvshows = df_tvshows.drop(['Directors'], axis = 1)
df_tvshows.fillna({'Directors' : "NA"}, inplace = True)
# Cast
df_tvshows.fillna({'Cast' : "NA"}, inplace = True)
# Genres
df_tvshows.fillna({'Genres': "NA"}, inplace = True)
# Country
df_tvshows.fillna({'Country': "NA"}, inplace = True)
# Language
df_tvshows.fillna({'Language': "NA"}, inplace = True)
# Plotline
df_tvshows.fillna({'Plotline': "NA"}, inplace = True)
# Runtime
# df_tvshows.fillna({'Runtime' : df_tvshows['Runtime'].mean()}, inplace = True)
# df_tvshows['Runtime'] = df_tvshows['Runtime'].astype(int)
df_tvshows.fillna({'Runtime' : "NA"}, inplace = True)
# Kind
# df_tvshows.fillna({'Kind': "NA"}, inplace = True)
# Type
# df_tvshows.fillna({'Type': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Type'], axis = 1)
# Seasons
# df_tvshows.fillna({'Seasons': 1}, inplace = True)
df_tvshows.fillna({'Seasons': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Seasons'], axis = 1)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
# df_tvshows.fillna({'Seasons' : df_tvshows['Seasons'].mean()}, inplace = True)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
# Service Provider
df_tvshows['Service Provider'] = df_tvshows.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_tvshows.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)
# Removing Duplicate and Missing Entries
df_tvshows.dropna(how = 'any', inplace = True)
df_tvshows.drop_duplicates(inplace = True)
data_investigate(df_tvshows)
No of Rows : 5432
No of Coloums : 21
**************************************************
Colums Names :
Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
'Service Provider'],
dtype='object')
**************************************************
Datatype of Columns :
ID int64
Title object
Year int64
Age object
IMDb object
Rotten Tomatoes object
Directors object
Cast object
Genres object
Country object
Language object
Plotline object
Runtime object
Kind object
Seasons object
Netflix int64
Hulu int64
Prime Video int64
Disney+ int64
Type int64
Service Provider object
dtype: object
**************************************************
Missing Values :
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :
ID 0.0
Title 0.0
Year 0.0
Age 0.0
IMDb 0.0
Rotten Tomatoes 0.0
Directors 0.0
Cast 0.0
Genres 0.0
Country 0.0
Language 0.0
Plotline 0.0
Runtime 0.0
Kind 0.0
Seasons 0.0
Netflix 0.0
Hulu 0.0
Prime Video 0.0
Disney+ 0.0
Type 0.0
Service Provider 0.0
dtype: float64
**************************************************
Pictorial Representation :
df_tvshows.head()
| ID | Title | Year | Age | IMDb | Rotten Tomatoes | Directors | Cast | Genres | Country | ... | Plotline | Runtime | Kind | Seasons | Netflix | Hulu | Prime Video | Disney+ | Type | Service Provider | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Snowpiercer | 2013 | 18 | 6.9 | 94 | NA | Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso... | Action,Drama,Sci-Fi,Thriller | United States | ... | Set seven years after the world has become a f... | 60 | tv series | 3 | 1 | 0 | 0 | 0 | 1 | Netflix |
| 1 | 2 | Philadelphia | 1993 | 13 | 8.8 | 80 | NA | Charlie Day,Glenn Howerton,Rob McElhenney,Kait... | Comedy | United States | ... | The gang, 5 raging alcoholic, narcissists run ... | 22 | tv series | 18 | 1 | 0 | 0 | 0 | 1 | Netflix |
| 2 | 3 | Roma | 2018 | 18 | 8.7 | 93 | NA | Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ... | Action,Drama,History,Romance,War | United Kingdom,United States | ... | In this British historical drama, the turbulen... | 52 | tv series | 2 | 1 | 0 | 0 | 0 | 1 | Netflix |
| 3 | 4 | Amy | 2015 | 18 | 7 | 87 | NA | Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma... | Drama | United States | ... | A family drama focused on three generations of... | 60 | tv series | 6 | 1 | 0 | 1 | 1 | 1 | Netflix |
| 4 | 5 | The Young Offenders | 2016 | NR | 8 | 100 | NA | Alex Murphy,Chris Walley,Hilary Rose,Dominic M... | Comedy | United Kingdom,Ireland | ... | NA | 30 | tv series | 3 | 1 | 0 | 0 | 0 | 1 | Netflix |
5 rows × 21 columns
df_tvshows.describe()
| ID | Year | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|
| count | 5432.000000 | 5432.000000 | 5432.000000 | 5432.000000 | 5432.000000 | 5432.000000 | 5432.0 |
| mean | 2716.500000 | 2010.668446 | 0.341311 | 0.293999 | 0.403351 | 0.033689 | 1.0 |
| std | 1568.227662 | 11.726176 | 0.474193 | 0.455633 | 0.490615 | 0.180445 | 0.0 |
| min | 1.000000 | 1901.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.0 |
| 25% | 1358.750000 | 2009.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.0 |
| 50% | 2716.500000 | 2014.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.0 |
| 75% | 4074.250000 | 2017.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 1.0 |
| max | 5432.000000 | 2020.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0 |
df_tvshows.corr()
| ID | Year | Netflix | Hulu | Prime Video | Disney+ | Type | |
|---|---|---|---|---|---|---|---|
| ID | 1.000000 | -0.031346 | -0.646330 | 0.034293 | 0.441264 | 0.195409 | NaN |
| Year | -0.031346 | 1.000000 | 0.222316 | -0.065807 | -0.198675 | -0.022741 | NaN |
| Netflix | -0.646330 | 0.222316 | 1.000000 | -0.366515 | -0.515086 | -0.119344 | NaN |
| Hulu | 0.034293 | -0.065807 | -0.366515 | 1.000000 | -0.377374 | -0.075701 | NaN |
| Prime Video | 0.441264 | -0.198675 | -0.515086 | -0.377374 | 1.000000 | -0.151442 | NaN |
| Disney+ | 0.195409 | -0.022741 | -0.119344 | -0.075701 | -0.151442 | 1.000000 | NaN |
| Type | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
# df_tvshows.sort_values('Year', ascending = True)
# df_tvshows.sort_values('IMDb', ascending = False)
# df_tvshows.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_otttvshows.csv', index = False)
# path = '/content/drive/MyDrive/Files/'
# udf_tvshows = pd.read_csv(path + 'updated_otttvshows.csv')
# udf_tvshows
# df_netflix_tvshows = df_tvshows.loc[(df_tvshows['Netflix'] > 0)]
# df_hulu_tvshows = df_tvshows.loc[(df_tvshows['Hulu'] > 0)]
# df_prime_video_tvshows = df_tvshows.loc[(df_tvshows['Prime Video'] > 0)]
# df_disney_tvshows = df_tvshows.loc[(df_tvshows['Disney+'] > 0)]
df_netflix_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 1) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_hulu_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 1) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_prime_video_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 1 ) & (df_tvshows['Disney+'] == 0)]
df_disney_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 1)]
df_tvshows_plotline = df_tvshows.copy()
df_tvshows_plotline.drop(df_tvshows_plotline.loc[df_tvshows_plotline['Plotline'] == "NA"].index, inplace = True)
# df_tvshows_plotline = df_tvshows_plotline[df_tvshows_plotline.Plotline != "NA"]
# Creating distinct dataframes only with the tvshows present on individual streaming platforms
netflix_plotline_tvshows = df_tvshows_plotline.loc[df_tvshows_plotline['Netflix'] == 1]
hulu_plotline_tvshows = df_tvshows_plotline.loc[df_tvshows_plotline['Hulu'] == 1]
prime_video_plotline_tvshows = df_tvshows_plotline.loc[df_tvshows_plotline['Prime Video'] == 1]
disney_plotline_tvshows = df_tvshows_plotline.loc[df_tvshows_plotline['Disney+'] == 1]
plt.figure(figsize = (10, 10))
corr = df_tvshows_plotline.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, allow annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()
<Figure size 720x720 with 0 Axes>
df_tvshows_plotline = df_tvshows_plotline['Plotline']
tvshows_plotline_w = ' '.join(df_tvshows_plotline)
stopwords = set(STOPWORDS)
wordcloud_all_plotline_tvshows = WordCloud(width = 1000, height = 500,
background_color ='white',
stopwords = stopwords,
min_font_size = 10).generate(tvshows_plotline_w)
# plot the WordCloud image
plt.figure(figsize = (20, 10), facecolor = None)
plt.imshow(wordcloud_all_plotline_tvshows)
plt.axis("off")
plt.tight_layout(pad = 0)
plt.show()
tvshows_plotline_w = tvshows_plotline_w.lower()
stop_words_english_tvshows = set(STOPWORDS)
word_tokens_english_tvshows = word_tokenize(tvshows_plotline_w)
filtered_tvshow_plotline = [w for w in word_tokens_english_tvshows if not w in stop_words_english_tvshows]
filtered_tvshow_plotline = " ".join(filtered_tvshow_plotline)
filtered_tvshow_plotline = re.sub("'s", '', filtered_tvshow_plotline)
filtered_tvshow_plotline = re.sub(r'[0-9]+', '', filtered_tvshow_plotline)
final_tvshow_plotline = re.sub(r'[^\w\s]', '', filtered_tvshow_plotline)
plotline_tvshows_corpus_len = len(filtered_tvshow_plotline.split())
plotline_tvshows_corpus_len
184538
def extract_ngrams(data, num):
n_grams = ngrams(nltk.word_tokenize(data), num)
return [ ' '.join(grams) for grams in n_grams]
plotline_ngram1_tvshows = FreqDist()
plotline_ngram1 = extract_ngrams(final_tvshow_plotline[:plotline_tvshows_corpus_len], 1)
for word in plotline_ngram1:
plotline_ngram1_tvshows[word.lower()] += 1
plotline_ngram1_tvshows.most_common(10)
[('life', 144),
('one', 129),
('new', 124),
('will', 116),
('family', 113),
('world', 98),
('years', 82),
('man', 69),
('young', 69),
('two', 69)]
plotline_ngram2_tvshows = FreqDist()
plotline_ngram2 = extract_ngrams(final_tvshow_plotline[:plotline_tvshows_corpus_len], 2)
for word in plotline_ngram2:
plotline_ngram2_tvshows[word.lower()] += 1
plotline_ngram2_tvshows.most_common(10)
[('new york', 30),
('york city', 11),
('high school', 11),
('best friend', 11),
('young man', 10),
('new life', 9),
('one day', 9),
('rainbow kingdom', 9),
('years later', 9),
('los angeles', 8)]
plotline_ngram3_tvshows = FreqDist()
plotline_ngram3 = extract_ngrams(final_tvshow_plotline[:plotline_tvshows_corpus_len], 3)
for word in plotline_ngram3:
plotline_ngram3_tvshows[word.lower()] += 1
plotline_ngram3_tvshows.most_common(10)
[('new york city', 11),
('cha eun sang', 5),
('living new york', 4),
('world war ii', 4),
('shah rukh khan', 3),
('true rainbow kingdom', 3),
('rainbow kingdom follows', 3),
('kingdom follows yearold', 3),
('follows yearold true', 3),
('yearold true best', 3)]
plotline_ngram4_tvshows = FreqDist()
plotline_ngram4 = extract_ngrams(final_tvshow_plotline[:plotline_tvshows_corpus_len], 4)
for word in plotline_ngram4:
plotline_ngram4_tvshows[word.lower()] += 1
plotline_ngram4_tvshows.most_common(10)
[('true rainbow kingdom follows', 3),
('rainbow kingdom follows yearold', 3),
('kingdom follows yearold true', 3),
('follows yearold true best', 3),
('yearold true best friend', 3),
('true best friend bartleby', 3),
('best friend bartleby cat', 3),
('friend bartleby cat help', 3),
('bartleby cat help whimsical', 3),
('cat help whimsical citizens', 3)]
plotline_ngram5_tvshows = FreqDist()
plotline_ngram5 = extract_ngrams(final_tvshow_plotline[:plotline_tvshows_corpus_len], 5)
for word in plotline_ngram5:
plotline_ngram5_tvshows[word.lower()] += 1
plotline_ngram5_tvshows.most_common(10)
[('true rainbow kingdom follows yearold', 3),
('rainbow kingdom follows yearold true', 3),
('kingdom follows yearold true best', 3),
('follows yearold true best friend', 3),
('yearold true best friend bartleby', 3),
('true best friend bartleby cat', 3),
('best friend bartleby cat help', 3),
('friend bartleby cat help whimsical', 3),
('bartleby cat help whimsical citizens', 3),
('cat help whimsical citizens rainbow', 3)]
# Netflix Wordcloud
netflix_plotline_tvshows_t = netflix_plotline_tvshows['Plotline']
netflix_tvshows_plotline_w = ' '.join(netflix_plotline_tvshows_t)
stopwords = set(STOPWORDS)
wordcloud_netflix_plotline_tvshows = WordCloud(width = 1000, height = 500,
background_color ='white',
stopwords = stopwords,
min_font_size = 10
).generate(netflix_tvshows_plotline_w)
print('\nThe Wordcloud Generated from Plotlines of Netflix is : \n')
# plot the WordCloud image
plt.figure(figsize = (20, 10), facecolor = None)
plt.imshow(wordcloud_netflix_plotline_tvshows)
plt.axis("off")
plt.tight_layout(pad = 0)
plt.show()
The Wordcloud Generated from Plotlines of Netflix is :
# Hulu Wordcloud
hulu_plotline_tvshows_t = hulu_plotline_tvshows['Plotline']
hulu_tvshows_plotline_w = ' '.join(hulu_plotline_tvshows_t)
stopwords = set(STOPWORDS)
wordcloud_hulu_plotline_tvshows = WordCloud(width = 1000, height = 500,
background_color ='white',
stopwords = stopwords,
min_font_size = 10
).generate(hulu_tvshows_plotline_w)
print('\nThe Wordcloud Generated from Plotlines of Hulu is : \n')
# plot the WordCloud image
plt.figure(figsize = (20, 10), facecolor = None)
plt.imshow(wordcloud_hulu_plotline_tvshows)
plt.axis("off")
plt.tight_layout(pad = 0)
plt.show()
The Wordcloud Generated from Plotlines of Hulu is :
# Prime Video Wordcloud
prime_video_plotline_tvshows_t = prime_video_plotline_tvshows['Plotline']
prime_video_tvshows_plotline_w = ' '.join(prime_video_plotline_tvshows_t)
stopwords = set(STOPWORDS)
wordcloud_prime_video_plotline_tvshows = WordCloud(width = 1000, height = 500,
background_color ='white',
stopwords = stopwords,
min_font_size = 10
).generate(prime_video_tvshows_plotline_w)
print('\nThe Wordcloud Generated from Plotlines of Prime Video is : \n')
# plot the WordCloud image
plt.figure(figsize = (20, 10), facecolor = None)
plt.imshow(wordcloud_prime_video_plotline_tvshows)
plt.axis("off")
plt.tight_layout(pad = 0)
plt.show()
The Wordcloud Generated from Plotlines of Prime Video is :
# Disney+ Wordcloud
disney_plotline_tvshows_t = disney_plotline_tvshows['Plotline']
disney_tvshows_plotline_w = ' '.join(disney_plotline_tvshows_t)
stopwords = set(STOPWORDS)
wordcloud_disney_plotline_tvshows = WordCloud(width = 1000, height = 500,
background_color ='white',
stopwords = stopwords,
min_font_size = 10
).generate(disney_tvshows_plotline_w)
print('\nThe Wordcloud Generated from Plotlines of Disney+ is : \n')
# plot the WordCloud image
plt.figure(figsize = (20, 10), facecolor = None)
plt.imshow(wordcloud_disney_plotline_tvshows)
plt.axis("off")
plt.tight_layout(pad = 0)
plt.show()
The Wordcloud Generated from Plotlines of Disney+ is :